Sectors Performance
Sector Price Performance Distribution
For Date: 2026-09-11
Performance Heatmap (%)
| Sector | 1 Day | 1 Week | 1 Month | 3 Months | 6 Months | YTD | 1 Year |
| Technology | 1.32 | 0.21 | 0.85 | 2.56 | 33.96 | 30.37 | 39.20 |
| Industrials | 1.07 | -1.65 | -7.18 | -1.35 | 2.23 | 9.68 | 14.26 |
| Communication Services | 0.99 | 0.51 | 1.20 | 0.69 | -3.21 | -3.12 | -2.04 |
| Consumer Discretionary | 0.89 | -1.70 | -5.27 | -2.68 | -0.63 | -4.17 | -4.09 |
| Real Estate | 0.86 | -1.16 | -1.50 | -2.49 | 3.98 | 9.21 | 5.59 |
| Financials | 0.67 | -1.46 | -0.95 | 9.17 | 16.31 | 5.12 | 7.61 |
| Materials | 0.37 | -2.84 | -4.30 | -0.16 | 3.05 | 11.37 | 12.03 |
| Consumer Staples | 0.35 | -1.42 | -1.55 | -1.54 | -0.19 | 8.67 | 6.32 |
| Energy | 0.32 | 1.69 | 6.91 | 14.87 | 15.89 | 44.66 | 50.68 |
| Health Care | -0.18 | -3.55 | -1.58 | 7.79 | 9.11 | 7.24 | 20.41 |
| Utilities | -0.31 | -1.60 | -2.84 | -3.15 | -6.96 | -0.52 | 2.42 |
Ask the market a question. Get a calculated answer.
The AI is not a chatbot bolted onto a document store. It calls the same analytics engine that powers every screen on this platform — so what comes back is a number it computed from raw history, with the command that produced it.
86,000+ instruments
Global equities, ETFs, funds, options, FX, commodities, crypto, economics, filings, transcripts and news — one normalised symbol universe with adjusted history.
A real analytics engine
Screening, backtesting, technicals, options analytics, correlations, seasonality and factor models — computed on demand from raw prices, never a stale cache.
It shows its working
Answers arrive with the charts, tables and tool calls behind them, so you can check the number instead of trusting a paraphrase.
Your own documents
Upload filings, decks and research. Ask across them and the answer cites the page it came from.
Agents and workflows
Multi-step research that runs the platform's tools for you — screen, pull the history, compute, compare, then write it up.
MCP, CLI and API
The same command catalogue from Claude, your own agent, a shell or your pipeline. The answer on screen is the answer your job gets at 4am.
You ask
“How does NVDA usually trade through earnings?”
It calls
→ ka.options_expected_move(NVDA)
It answers
NVDA has averaged a 9.2% absolute move on the day after earnings and closed higher 67% of the time. Two in three reactions land between −4.2% and +16.3% — the distribution is skewed right, not symmetric.
Every figure computed live from our own history — not scraped, not summarised.
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